Text region detection may be employed by a computing device to support a variety of functionality. In one example, the computing device may first determine a location, at which, text is located within a digital image. Optical character recognition techniques are then be employed by the computing device to identify the text at the location. This text is then be used by the computing device as a basis to locate the digital image (e.g., as part of a keyword search), editing or modification of the text (e.g., a translation), and so forth.
Conventional text region detection techniques employed by the computing device are confronted with a variety of challenges. For example, detection of text from a digital image of a natural image scene (e.g., a sign in a landscape) involves numerous challenges due to the amount of diversity in both text appearance and surrounding backgrounds that are exhibited by these images. Text lines in natural images, for instance, that are used as a basis to identify a location of text may be disposed in a variety of orientations, fonts, sizes, and colors across a variety of digital images. Additionally, objects in such image scenes may include text-like properties that result in false-positives, e.g., windows, bricks, fences, branches of a tree, and so forth. As a result, conventional techniques employed by a computing device typically provide a significant amount of false positives, and thus result in image region detection inaccuracies and inefficient use of computational resources by the computing device in attempting to detect these regions. Further, this lack of accuracy and computational inefficiency may have a direct effect on techniques used by the computing device that rely on text region detection, such as for a keyword search, image editing and translation, and so forth.